{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train : (768, 9)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "train = pd.read_csv('pima-indians-diabetes.csv')\n",
    "print(\"Train :\" , train.shape)\n",
    "\n",
    "''' \n",
    "# 打印数据基本信息\n",
    "train.info()\n",
    "<class 'pandas.core.frame.DataFrame'>\n",
    "RangeIndex: 768 entries, 0 to 767\n",
    "Data columns (total 9 columns):\n",
    "pregnants                       768 non-null int64\n",
    "Plasma_glucose_concentration    768 non-null int64\n",
    "blood_pressure                  768 non-null int64\n",
    "Triceps_skin_fold_thickness     768 non-null int64\n",
    "serum_insulin                   768 non-null int64\n",
    "BMI                             768 non-null float64\n",
    "Diabetes_pedigree_function      768 non-null float64\n",
    "Age                             768 non-null int64\n",
    "Target                          768 non-null int64\n",
    "dtypes: float64(2), int64(7)\n",
    "memory usage: 54.1 KB \n",
    "该数据集已知存在缺失值，某些列中存在的缺失值被标记为0。\n",
    "通过这些列中指标的定义和相应领域的常识可以证实上述观点，\n",
    "譬如体重指数和血压两列中的0作为指标数值来说是无意义的。\n",
    "'''\n",
    "\n",
    "'''\n",
    "train.describe()\n",
    "从结果中我们可以看到很多列的最小值为0。\n",
    "而在一些特定列代表的变量中，0值并没有意义，\n",
    "这就表名该值无效或为缺失值。\n",
    "\n",
    "具体来说，下列变量的最小值为0时数据无意义： \n",
    "1、血浆葡萄糖浓度 \n",
    "2、舒张压 \n",
    "3、肱三头肌皮褶厚度 \n",
    "4、餐后血清胰岛素 \n",
    "5、体重指数\n",
    "'''\n",
    "\n",
    "NaN_col_names = ['Plasma_glucose_concentration', 'blood_pressure', 'Triceps_skin_fold_thickness', 'serum_insulin', 'BMI']\n",
    "'''\n",
    "print((train[NaN_col_names] == 0).sum())\n",
    "Train : (768, 9)\n",
    "Plasma_glucose_concentration      5\n",
    "blood_pressure                   35\n",
    "Triceps_skin_fold_thickness     227\n",
    "serum_insulin                   374\n",
    "BMI                              11\n",
    "dtype: int64\n",
    "'''\n",
    "\n",
    "'''\n",
    "第1、2、5列中0值较少，相比较而言，\n",
    "第3、4列中的0值多出数倍，接近总量的一半。 \n",
    "为了确保有足够的数据量来训练模型，\n",
    "针对不同的列需要有不同的缺失值判断策略。\n",
    "'''\n",
    "# 查看每个变量的分布及其与标签之间的关系\n",
    "#该问题为分类问题，类别型特征值直方图可以用countplot\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 怀孕次数\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怀孕次数17次看起来异常，但是在疾病诊断中，异常可能代表生病，先不要删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a192e6940>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='pregnants', hue='Target', data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从数据看出，怀孕次数越多，生病的概率越高。所以不能把怀孕超过10次的人删除"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plasma_glucose_concentration\n",
    "血浆葡萄糖浓度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a4c5f60>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Plasma_glucose_concentration, kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Plasma_glucose_concentration')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a64f908>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# blood_pressure\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.blood_pressure, kde = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "血压为0？ 这里可以考虑使用一些插值法去弥补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'blood_pressure')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看血压与糖尿病的关系\n",
    "sns.violinplot(x = 'Target', y = 'blood_pressure', data=train, hue='Target')\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('blood_pressure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来关联不大\n",
    "后面的一些特征就不做赘述了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ac77550>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征相关性\n",
    "data_coor = train.corr().abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_coor,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "'''数据中很多列的最小值为0。而在一些特定列代表的变量中，0值并没有意义，这就表名该值无效或为缺失值。\n",
    "\n",
    "具体来说，下列变量的最小值为0时数据无意义： 1、血浆葡萄糖浓度 2、舒张压 3、肱三头肌皮褶厚度 4、餐后血清胰岛素 5、体重指数\n",
    "\n",
    "在Pandas的DataFrame中，通过replace()函数可以很方便的将我们感兴趣的数据子集的值标记为NaN。\n",
    "\n",
    "标记完缺失值之后，可以利用isnull()函数将数据集中所有的NaN值标记为True，然后就可以得到每一列中缺失值的数量了。'''\n",
    "\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0, np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对缺失值较多的特征,新增一个特征，表示这个特征是否缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ac2cdd8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 缺失值较多，新增一个缺失字段\n",
    "train['Triceps_skin_fold_thickness_Missing'] = train['Triceps_skin_fold_thickness'].apply(lambda x:1 if pd.isnull(x) else 0)\n",
    "train[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing']].head(10)\n",
    "sns.countplot(x=\"Triceps_skin_fold_thickness_Missing\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1aba7e48>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#缺失值比较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "train['serum_insulin_Missing'] = train['serum_insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"serum_insulin_Missing\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不过特征是否缺失好像和目标也没什么关系 感觉特征缺失是随机的，将这新增的特征删除，老实用中值填补算了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         3.0000\n",
      "Plasma_glucose_concentration    117.0000\n",
      "blood_pressure                   72.0000\n",
      "Triceps_skin_fold_thickness      29.0000\n",
      "serum_insulin                   125.0000\n",
      "BMI                              32.3000\n",
      "Diabetes_pedigree_function        0.3725\n",
      "Age                              29.0000\n",
      "Target                            0.0000\n",
      "dtype: float64\n",
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train.drop([\"Triceps_skin_fold_thickness_Missing\", \"serum_insulin_Missing\"], axis=1, inplace=True)\n",
    "medians = train.median() \n",
    "print(medians)\n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get labels\n",
    "y_train = train['Target']\n",
    "X_train = train.drop([\"Target\"], axis=1)\n",
    "\n",
    "#用于保存特征工程\n",
    "feat_names = X_train.columns\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征处理结果存为文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 存为csv格式\n",
    "X_train = pd.DataFrame(columns = feat_names, data = X_train)\n",
    "\n",
    "train = pd.concat((X_train, y_train), axis = 1)\n",
    "\n",
    "train.to_csv('FE_pima-indians-diabetes.csv', index=False, header=True)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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